TY - GEN
T1 - CNN-Based Intelligent 3D Path Planning Algorithm in the Framework of the Improved Lazy Theta*
AU - Yin, Yuwan
AU - Ning, Xin
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In this paper, a CNN-based intelligent path planning algorithm in the framework of the improved Lazy theta* is proposed to solve the problem of path planning in the 3D terrain environment. The key point of the proposed algorithm is that the safety factor and the total length of the path will be comprehensively considered. By considering these two factors, a short, safe and smooth path can be planned efficiently and automatically in a 3D terrain environment. In order to solve the problem of the path moving close to the edge of the obstacles and passing dangerously between multiple obstacles, CNN is used to create a continuous and safe 3D topographic map and improve the ways of node expansion to ensure the safety of the path. Moreover, a weight self-adjustment strategy is introduced to optimize the path cost function, which solves the problem of the low search efficiency. The simulation results show that compared with the ordinary A* algorithm and Lazy theta* algorithm, the path planned by the improved intelligent Lazy theta* algorithm proposed in this paper is safer and smoother, and the search efficiency is higher, which can be applied to different planning objects according to different task scenarios.
AB - In this paper, a CNN-based intelligent path planning algorithm in the framework of the improved Lazy theta* is proposed to solve the problem of path planning in the 3D terrain environment. The key point of the proposed algorithm is that the safety factor and the total length of the path will be comprehensively considered. By considering these two factors, a short, safe and smooth path can be planned efficiently and automatically in a 3D terrain environment. In order to solve the problem of the path moving close to the edge of the obstacles and passing dangerously between multiple obstacles, CNN is used to create a continuous and safe 3D topographic map and improve the ways of node expansion to ensure the safety of the path. Moreover, a weight self-adjustment strategy is introduced to optimize the path cost function, which solves the problem of the low search efficiency. The simulation results show that compared with the ordinary A* algorithm and Lazy theta* algorithm, the path planned by the improved intelligent Lazy theta* algorithm proposed in this paper is safer and smoother, and the search efficiency is higher, which can be applied to different planning objects according to different task scenarios.
KW - 3D path planning
KW - Convolutional neural network
KW - Lazy Theta
UR - http://www.scopus.com/inward/record.url?scp=85130875889&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-9492-9_46
DO - 10.1007/978-981-16-9492-9_46
M3 - 会议稿件
AN - SCOPUS:85130875889
SN - 9789811694912
T3 - Lecture Notes in Electrical Engineering
SP - 457
EP - 466
BT - Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021
A2 - Wu, Meiping
A2 - Niu, Yifeng
A2 - Gu, Mancang
A2 - Cheng, Jin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2021
Y2 - 24 September 2021 through 26 September 2021
ER -